Files
lerobot/examples/phone_to_so100/record.py
T
Steven Palma ce665160ae feat(processor): multiple improvements to the pipeline porting (#1749)
* [Port codebase pipeline] General fixes for RL and scripts (#1748)

* Refactor dataset configuration in documentation and codebase

- Updated dataset configuration keys from `dataset_root` to `root` and `num_episodes` to `num_episodes_to_record` for consistency.
- Adjusted replay episode handling by renaming `episode` to `replay_episode`.
- Enhanced documentation
- added specific processor to transform from policy actions to delta actions

* Added Robot action to tensor processor
Added new processor script for dealing with gym specific action processing

* removed RobotAction2Tensor processor; imrpoved choosing observations in actor

* nit in delta action

* added missing reset functions to kinematics

* Adapt teleoperate and replay to pipeline similar to record

* refactor(processors): move to inheritance (#1750)

* fix(teleoperator): improvements phone implementation (#1752)

* fix(teleoperator): protect shared state in phone implementation

* refactor(teleop): separate classes in phone

* fix: solve breaking changes (#1753)

* refactor(policies): multiple improvements (#1754)

* refactor(processor): simpler logic in device processor (#1755)

* refactor(processor): euclidean distance in delta action processor (#1757)

* refactor(processor): improvements to joint observations processor migration (#1758)

* refactor(processor): improvements to tokenizer migration (#1759)

* refactor(processor): improvements to tokenizer migration

* fix(tests): tokenizer tests regression from #1750

* fix(processors): fix float comparison and config in hil processors (#1760)

* chore(teleop): remove unnecessary callbacks in KeyboardEndEffectorTeleop (#1761)

* refactor(processor): improvements normalize pipeline migration (#1756)

* refactor(processor): several improvements normalize processor step

* refactor(processor): more improvements normalize processor

* refactor(processor): more changes to normalizer

* refactor(processor): take a different approach to DRY

* refactor(processor): final design

* chore(record): revert comment and continue deleted (#1764)

* refactor(examples): pipeline phone examples (#1769)

* refactor(examples): phone teleop + teleop script

* refactor(examples): phone replay + replay

* chore(examples): rename phone example files & folders

* feat(processor): fix improvements to the pipeline porting (#1796)

* refactor(processor): enhance tensor device handling in normalization process (#1795)

* refactor(tests): remove unsupported device detection test for complementary data (#1797)

* chore(tests): update ToBatchProcessor test (#1798)

* refactor(tests): remove in-place mutation tests for actions and complementary data in batch processor

* test(tests): add tests for action and task processing in batch processor

* add names for android and ios phone (#1799)

* use _tensor_stats in normalize processor (#1800)

* fix(normalize_processor): correct device reference for tensor epsilon handling (#1801)

* add point 5 add missing feature contracts (#1806)

* Fix PR comments 1452 (#1807)

* use key to determine image

* Address rest of PR comments

* use PolicyFeatures in transform_features

---------

Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>

---------

Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>
2025-08-31 20:38:52 +02:00

216 lines
7.2 KiB
Python

# !/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features
from lerobot.datasets.utils import merge_features
from lerobot.model.kinematics import RobotKinematics
from lerobot.processor.converters import (
to_output_robot_action,
to_transition_robot_observation,
to_transition_teleop_action,
)
from lerobot.processor.pipeline import RobotProcessor
from lerobot.record import record_loop
from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
from lerobot.robots.so100_follower.robot_kinematic_processor import (
AddRobotObservationAsComplimentaryData,
EEBoundsAndSafety,
EEReferenceAndDelta,
ForwardKinematicsJointsToEE,
GripperVelocityToJoint,
InverseKinematicsEEToJoints,
)
from lerobot.robots.so100_follower.so100_follower import SO100Follower
from lerobot.teleoperators.phone.config_phone import PhoneConfig, PhoneOS
from lerobot.teleoperators.phone.phone_processor import MapPhoneActionToRobotAction
from lerobot.teleoperators.phone.teleop_phone import Phone
from lerobot.utils.control_utils import init_keyboard_listener
from lerobot.utils.utils import log_say
from lerobot.utils.visualization_utils import _init_rerun
NUM_EPISODES = 10
FPS = 30
EPISODE_TIME_SEC = 60
RESET_TIME_SEC = 30
TASK_DESCRIPTION = "My task description"
HF_REPO_ID = "<hf_username>/<dataset_repo_id>"
# Initialize the robot and teleoperator
camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
robot_config = SO100FollowerConfig(
port="/dev/tty.usbmodem58760434471",
id="my_awesome_follower_arm",
cameras=camera_config,
use_degrees=True,
)
teleop_config = PhoneConfig(phone_os=PhoneOS.IOS) # or PhoneOS.ANDROID
# Initialize the robot and teleoperator
robot = SO100Follower(robot_config)
phone = Phone(teleop_config)
# NOTE: It is highly recommended to use the urdf in the SO-ARM100 repo: https://github.com/TheRobotStudio/SO-ARM100/blob/main/Simulation/SO101/so101_new_calib.urdf
kinematics_solver = RobotKinematics(
urdf_path="./src/lerobot/teleoperators/sim/so101_new_calib.urdf",
target_frame_name="gripper_frame_link",
joint_names=list(robot.bus.motors.keys()),
)
# Build pipeline to convert phone action to ee pose action
phone_to_robot_ee_pose = RobotProcessor(
steps=[
MapPhoneActionToRobotAction(platform=teleop_config.phone_os),
AddRobotObservationAsComplimentaryData(robot=robot),
EEReferenceAndDelta(
kinematics=kinematics_solver,
end_effector_step_sizes={"x": 0.5, "y": 0.5, "z": 0.5},
motor_names=list(robot.bus.motors.keys()),
),
EEBoundsAndSafety(
end_effector_bounds={"min": [-1.0, -1.0, -1.0], "max": [1.0, 1.0, 1.0]},
max_ee_step_m=0.20,
max_ee_twist_step_rad=0.50,
),
],
to_transition=to_transition_teleop_action,
to_output=lambda tr: tr,
)
# Build pipeline to convert ee pose action to joint action
robot_ee_to_joints = RobotProcessor(
steps=[
InverseKinematicsEEToJoints(
kinematics=kinematics_solver,
motor_names=list(robot.bus.motors.keys()),
initial_guess_current_joints=True,
),
GripperVelocityToJoint(
motor_names=list(robot.bus.motors.keys()),
speed_factor=20.0,
),
],
to_transition=lambda tr: tr,
to_output=to_output_robot_action,
)
# Build pipeline to convert joint observation to ee pose observation
robot_joints_to_ee_pose = RobotProcessor(
steps=[
ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
],
to_transition=to_transition_robot_observation,
to_output=lambda tr: tr,
)
# Build dataset ee action features
action_ee = aggregate_pipeline_dataset_features(
pipeline=phone_to_robot_ee_pose,
initial_features=phone.action_features,
use_videos=True,
patterns=["action.ee"],
)
# Get gripper pos action features
gripper = aggregate_pipeline_dataset_features(
pipeline=robot_ee_to_joints,
initial_features={},
use_videos=True,
patterns=["action.gripper.pos", "observation.state.gripper.pos"],
)
# Build dataset ee observation features
observation_ee = aggregate_pipeline_dataset_features(
pipeline=robot_joints_to_ee_pose,
initial_features=robot.observation_features,
use_videos=True,
patterns=["observation.state.ee"],
)
dataset_features = merge_features(action_ee, gripper, observation_ee)
print("All dataset features: ", dataset_features)
# Create the dataset
dataset = LeRobotDataset.create(
repo_id=HF_REPO_ID,
fps=FPS,
features=dataset_features,
robot_type=robot.name,
use_videos=True,
image_writer_threads=4,
)
# Initialize the keyboard listener and rerun visualization
_, events = init_keyboard_listener()
_init_rerun(session_name="recording_phone")
# Connect the robot and teleoperator
robot.connect()
phone.connect()
episode_idx = 0
while episode_idx < NUM_EPISODES and not events["stop_recording"]:
log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
record_loop(
robot=robot,
events=events,
fps=FPS,
teleop=phone,
dataset=dataset,
control_time_s=EPISODE_TIME_SEC,
single_task=TASK_DESCRIPTION,
display_data=True,
teleop_action_processor=phone_to_robot_ee_pose,
robot_action_processor=robot_ee_to_joints,
robot_observation_processor=robot_joints_to_ee_pose,
)
# Reset the environment if not stopping or re-recording
if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
log_say("Reset the environment")
record_loop(
robot=robot,
events=events,
fps=FPS,
teleop=phone,
control_time_s=RESET_TIME_SEC,
single_task=TASK_DESCRIPTION,
display_data=True,
teleop_action_processor=phone_to_robot_ee_pose,
robot_action_processor=robot_ee_to_joints,
robot_observation_processor=robot_joints_to_ee_pose,
)
if events["rerecord_episode"]:
log_say("Re-recording episode")
events["rerecord_episode"] = False
events["exit_early"] = False
dataset.clear_episode_buffer()
continue
dataset.save_episode()
episode_idx += 1
# Clean up
log_say("Stop recording")
robot.disconnect()
phone.disconnect()
dataset.push_to_hub()